Course Description
This course is targeted at individuals who want to understand what healthcare analytics is, how data analytics can be used in healthcare, and how it is implemented. The course has 5 parts:
Analytics Primer
Areas Currently Utilizing Predictive Analytics
Healthcare Transformation Model
Starting a Predictive Analytics Program
The Analytics Program Lifecycle
Challenges to Implementing Analytics
What am I going to get from this course?
Understand what healthcare analytics is, identify the 5-stage Analytics Program Lifecycle, and identify four types of analytics (i.e., descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics) and several areas currently benefiting from predictive analytics.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
No technical knowledge is needed. This course is an introduction and an overview of healthcare analytics.
Who should take this course? Who should not?
Anyone who wants an easy introduction to healthcare analytics and gain understanding of the technology components.
Curriculum
Module 1: Analytics Primer
04:56
Lecture 1
What Are Four Types of Analytics?
03:30
Introduces four types of analytics (i.e., descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics).
Lecture 2
How Can Analytics Be Used in Healthcare?
01:26
Gives examples of how analytics can be used in healthcare.
Module 2: Areas Currently Utilizing Predictive Analytics
10:17
Lecture 3
Several Areas Currently Benefiting from Predictive Analytics
01:01
Introduces several areas currently benefiting from predictive analytics.
Lecture 4
Reduced Readmissions
03:31
Describes how predictive analytics can help reduce readmissions.
Lecture 5
Disease Outbreak Predictions
00:30
Describes how predictive analytics can be used to foresee an increase in influenza cases.
Lecture 6
Improving Patient Flow
02:20
Describes how predictive analytics can be used to manage hospital admissions and discharges.
Lecture 7
Emergency Room Uses
02:55
Describes how predictive analytics can be used to predict whether an emergency room patient is likely to: go into cardiac arrest; suffer a stroke; or potentially suffer from sepsis shock.
Module 3: Healthcare Transformation Model
05:52
Lecture 8
What is the Healthcare Transformation Change Model?
01:58
Introduces the healthcare transformation change model.
Lecture 9
What are Three Continuums to the Healthcare Transformation Change Model?
03:54
Introduces three continuums to the healthcare transformation change model (i.e., organization/people, data/technology, process/workflow).
Module 4: Starting a Predictive Analytics Program
04:40
Lecture 10
A Successful Predictive Analytics Implementation
04:40
Describes a successful predictive analytics implementation.
Module 5: The Analytics Program Lifecycle
02:52
Lecture 11
What is the 5-stage Analytics Program Lifecycle (APL)?
00:55
Introduces the 5-stage Analytics Program Lifecycle (APL).
Lecture 12
Pre-Analysis
00:25
Describes the initial research stage.
Lecture 13
Data Gathering
00:31
Describes data gathering.
Lecture 14
Execution
00:15
Lecture 15
Post-Analysis
00:30
Lecture 16
Adjustment
00:16
Describes the adjustment stage.
Module 6: Challenges to Implementing Analytics
02:59
Lecture 17
Leadership Challenges
01:12
Introduces leadership challenges.
Lecture 18
Data Management Challenges
00:31
Introduces data management challenges.
Lecture 19
Talent Challenges
01:16
Introduces talent challenges.
